| """ ChatGLM model configuration """ | |
| from transformers.configuration_utils import PretrainedConfig | |
| from transformers.utils import logging | |
| logger = logging.get_logger(__name__) | |
| class ChatGLMConfig(PretrainedConfig): | |
| r""" | |
| This is the configuration class to store the configuration of a [`~ChatGLMModel`]. | |
| It is used to instantiate an ChatGLM model according to the specified arguments, defining the model | |
| architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of | |
| the ChatGLM-6B [THUDM/ChatGLM-6B](https://huggingface.co/THUDM/chatglm-6b) architecture. | |
| Configuration objects inherit from [`PretrainedConfig`] and can be used | |
| to control the model outputs. Read the documentation from [`PretrainedConfig`] | |
| for more information. | |
| Args: | |
| vocab_size (`int`, *optional*, defaults to 130528): | |
| Vocabulary size of the ChatGLM-6B model. Defines the number of different tokens that can be represented by the | |
| `inputs_ids` passed when calling [`~ChatGLMModel`] or | |
| [`~TFChatGLMModel`]. | |
| hidden_size (`int`, *optional*, defaults to 4096): | |
| Dimension of the encoder layers and the pooler layer. | |
| num_hidden_layers (`int`, *optional*, defaults to 28): | |
| Number of hidden layers in the Transformer encoder. | |
| num_attention_heads (`int`, *optional*, defaults to 32): | |
| Number of attention heads for each attention layer in the Transformer encoder. | |
| inner_hidden_size (`int`, *optional*, defaults to 16384): | |
| Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. | |
| max_sequence_length (`int`, *optional*, defaults to 512): | |
| The maximum sequence length that this model might ever be used with. | |
| Typically set this to something large just in case (e.g., 512 or 1024 or 2048). | |
| layernorm_epsilon (`float`, *optional*, defaults to 1e-5): | |
| The epsilon used by the layer normalization layers. | |
| use_cache (`bool`, *optional*, defaults to `True`): | |
| Whether the model should return the last key/values attentions (not used by all models). | |
| Example: | |
| ```python | |
| >>> from configuration_chatglm import ChatGLMConfig | |
| >>> from modeling_chatglm import ChatGLMModel | |
| >>> # Initializing a ChatGLM-6B THUDM/ChatGLM-6B style configuration | |
| >>> configuration = ChatGLMConfig() | |
| >>> # Initializing a model from the THUDM/ChatGLM-6B style configuration | |
| >>> model = ChatGLMModel(configuration) | |
| >>> # Accessing the model configuration | |
| >>> configuration = model.config | |
| ``` | |
| """ | |
| model_type = "chatglm" | |
| def __init__( | |
| self, | |
| vocab_size=130528, | |
| hidden_size=4096, | |
| num_layers=28, | |
| num_attention_heads=32, | |
| layernorm_epsilon=1e-5, | |
| use_cache=False, | |
| bos_token_id=130004, | |
| eos_token_id=130005, | |
| pad_token_id=0, | |
| max_sequence_length=2048, | |
| inner_hidden_size=16384, | |
| position_encoding_2d=True, | |
| quantization_bit=0, | |
| quantization_embeddings=False, | |
| pre_seq_len=None, | |
| prefix_projection=False, | |
| **kwargs | |
| ): | |
| self.num_layers = num_layers | |
| self.vocab_size = vocab_size | |
| self.hidden_size = hidden_size | |
| self.num_attention_heads = num_attention_heads | |
| self.max_sequence_length = max_sequence_length | |
| self.layernorm_epsilon = layernorm_epsilon | |
| self.inner_hidden_size = inner_hidden_size | |
| self.use_cache = use_cache | |
| self.bos_token_id = bos_token_id | |
| self.eos_token_id = eos_token_id | |
| self.pad_token_id = pad_token_id | |
| self.position_encoding_2d = position_encoding_2d | |
| self.quantization_bit = quantization_bit | |
| self.quantization_embeddings = quantization_embeddings | |
| self.pre_seq_len = pre_seq_len | |
| self.prefix_projection = prefix_projection | |
| super().__init__( | |
| pad_token_id=pad_token_id, | |
| bos_token_id=bos_token_id, | |
| eos_token_id=eos_token_id, | |
| **kwargs | |
| ) | |